Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.

Filippi CG ，Stein JM ，Wang Z ，Bakas S ，Liu Y ，Chang PD ，Lui Y ，Hess C ，Barboriak DP ，Flanders AE ，Wintermark M ，Zaharchuk G ，Wu O ... -  《-》

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Ueda D ，Kakinuma T ，Fujita S ，Kamagata K ，Fushimi Y ，Ito R ，Matsui Y ，Nozaki T ，Nakaura T ，Fujima N ，Tatsugami F ，Yanagawa M ，Hirata K ，Yamada A ，Tsuboyama T ，Kawamura M ，Fujioka T ，Naganawa S ... -  《-》

Understanding Bias in Artificial Intelligence: A Practice Perspective.

Davis MA ，Wu O ，Ikuta I ，Jordan JE ，Johnson MH ，Quigley E ... -  《-》

Multidisciplinary considerations of fairness in medical AI: A scoping review.

Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature. This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as "artificial intelligence," "machine learning," "medical," "algorithm," "fairness," "decision-making," and "bias." The collected data were charted, synthesized, and subjected to descriptive and thematic analysis. After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness. Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.

Wang Y ，Song Y ，Ma Z ，Han X ... -  《-》

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

Economou-Zavlanos NJ ，Bessias S ，Cary MP Jr ，Bedoya AD ，Goldstein BA ，Jelovsek JE ，O'Brien CL ，Walden N ，Elmore M ，Parrish AB ，Elengold S ，Lytle KS ，Balu S ，Lipkin ME ，Shariff AI ，Gao M ，Leverenz D ，Henao R ，Ming DY ，Gallagher DM ，Pencina MJ ，Poon EG ... -  《-》